Artificial Intelligence (ai) Based Generation of Data Presentations

1. An Artificial Intelligence (AI) based data narration generating system comprising: at least one processor; a non-transitory computer readable medium storing machine-readable instructions that cause the at least one processor to: access one or more data sources to be used for generating a narration, wherein the narration includes a presentation of data from the one or more data sources in one or more of a plurality of formats; select columns from the data that are to be included in the narration from the data sources based on an input from an administrative user; provide the data to a plurality of machine learning (ML) models trained in generation of one or more snippets, wherein each snippet includes a presentation of at least a subset of the data in one of the plurality of formats; determine based at least on corresponding data types of each of the columns, summarization and aggregation attributes of data in the columns; enable generation of a plurality of the snippets by at least a subset of the plurality ML models in one or more formats of the plurality of formats that correspond to the subset of ML models generating the snippets; automatically select at least a subset of the plurality of the snippets via a voting process for presentation to the administrative user, wherein the automatic selection is based at least on quality of data including the summarization and the aggregation attributes of the data in the columns; enable presentation of the selected subset of snippets to the administrative user via an input/output graphical user interface (GUI); receive selection of one or more snippets by the administrative user from the subset of snippets; and generate the narration including the user selected snippets arranged in accordance with an order as received from the administrative user.

2. The narration generating system of claim 1 , further comprising instructions that cause the processor to: access training data for training the plurality of ML models in generating the snippets, wherein the training data includes different data sets and corresponding snippets that present the data sets in a preferred one of the plurality of formats.

3. The narration generating system of claim 1 , further comprising instructions that cause the processor to: enable the administrative user to provide access to the data narration to one or more end users based on privileges mapped to profiles of the end users.

4. The narration generating system of claim 3 , wherein the instructions for enabling the administrative user to provide access to the narration to one or more end users further comprising instructions that cause the processor to: provide access to views of the data narration to the end users, wherein the views provided to the end users include corresponding portions of the data that the end users are permitted to view based on the privileges.

5. The narration generating system of claim 1 , further comprising instructions that cause the processor to: collect feedback from the administrative user and end users for the narration.

6. The narration generating system of claim 1 , wherein the plurality of formats include a textual format, a data structure format and a graphical format.

7. The narration generating system of claim 6 , wherein the instructions for automatically selecting at least a subset of the plurality of the snippets further comprising instructions that cause the processor to: enable generation of at least one of the plurality of the snippets by each of the plurality ML models in one of the plurality of formats.

8. The narration generating system of claim 7 , wherein the instructions for automatically selecting at least the subset of the plurality of the snippets further comprising instructions that cause the processor to: automatically select the subset of the plurality of the snippets based on quality of the data, volume of the data and the voting process based on user feedback.

9. The narration generating system of claim 1 , wherein a textual format includes a natural language summary obtained from the data that is to be included in a corresponding snippet.

10. The narration generating system of claim 1 , wherein the plurality of ML models include recurrent neural network (RNN), Key Press Markup Language (KPML) and simplenlg.

11. A method of generating data narrations, comprising: selecting data that is to be included in a data narration from one or more data sources, the selection of data is based on an input from an administrative user and the data narration includes a presentation of data from the one or more data sources in one or more of a plurality of formats; providing the data to a plurality of machine learning (ML) models trained in generation of a plurality of snippets, wherein each snippet includes a presentation of at least a subset of the data in one of the plurality of formats; determining, by each of the plurality of ML models, a subset of the data to be included in the snippets and one of the plurality of formats of presenting the subset of the data in the snippets; determining based at least on corresponding data types of each of the columns, summarization and aggregation attributes of data in the columns; enabling generation of each of the plurality of the snippets by at least one ML model of the plurality ML models in a format of the plurality of formats that correspond to the ML model generating the snippet; automatically selecting at least a subset of the plurality of snippets for presentation to the administrative user via an input/output graphical user interface (I/O GUI), the automatic selection based on a quality of the data, quantity of the data and a voting mechanism wherein the quality of data includes the summarization and the aggregation attributes of the data in the columns; receiving selection of one or more of the subset of snippets by the administrative user via the output GUI; generating the data narration including the user selected snippets arranged in accordance with an order as received from the administrative user; and enabling access to the narration to a plurality of end users based on respective user profiles of the end users, wherein the user profiles determine one or more of the user selected snippets that are displayed to each of the end users in the data narration.

12. The method of claim 11 , further comprising: including, within the data narration, the voting mechanism that enables the end users to provide feedback to the data narration.

13. The method of claim 11 , wherein automatically selecting at least the subset of the plurality of snippets further comprising: automatically selecting at least a subset of the plurality of snippets based on historical feedback received from the end users to prior narrations.

14. The method of claim 11 , wherein automatically selecting at least the subset of the plurality of snippets further comprising: accessing configuration input from the administrative user regarding a number of snippets to be automatically selected; and selecting, based on the configuration input, a highest scoring snippet of the plurality of snippets for presentation via the output GUI.

15. The method of claim 11 , wherein receiving the selection of the subset of snippets further comprising: receiving, via drag-and-drop operations, selection of one or more of the subset of snippets by the administrative user via the output GUI.

16. The method of claim 11 , further comprising: enabling, the administrative user to edit the data to be included in the narration.

17. A non-transitory computer-readable storage medium comprising machine-readable instructions that cause a processor to: access one or more data sources to be used for generating a narration, wherein the narration includes a presentation of data from the one or more data sources in one or more of a plurality of formats; select columns from the data that are to be included in the narration from the data sources based on an input from an administrative user; provide data from the selected columns to a plurality of machine learning (ML) models trained in generation of one or more snippets, wherein each snippet includes a presentation of at least a subset of the data in one of the plurality of formats; determine, by each of the plurality of ML models, summarization and aggregation attributes of data in the columns; enable generation of a plurality of the snippets by at least a subset of the plurality ML models in one or more corresponding formats of the plurality of formats based on the summarization and aggregation attributes of the data in the columns; automatically select at least a subset of the plurality of snippets via a voting process for presentation to the administrative user via an output graphical user interface (GUI), wherein the automatic selection is based at least on quality of data including the summarization and the aggregation attributes of the data in the columns; enable presentation of the selected subset of snippets to the administrative user via the output GUI; receive selection of one or more snippets by the administrative user from the subset of snippets; and generate the narration including the user selected snippets arranged in accordance with an order as received from the administrative user.

18. The non-transitory medium of claim 17 , wherein the instructions for to enable generation of a plurality of the snippets by the plurality ML models further comprising instructions that cause the processor to: transmit an output including the summarization and aggregate attributes of data in the columns to one or more Application Programming Interfaces (APIs), wherein the APIs include textual API, graph APIs and data structure API.

19. The non-transitory medium of claim 17 , further comprising instructions that cause the processor to: receive a request from a device associated with an end user for access to the data narration; and determine data that is permissible for viewing by the end user based on a profile associated with the end user.

20. The non-transitory medium of claim 19 , further comprising instructions that cause the processor to: generate permissible snippets including the data that is permissible for viewing by the end user, the permissible snippets form a subset of the plurality of snippets; and generate the data narration including the permissible snippets.